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Genome Biology 2009, 10:R76
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2009Edwardset al.Volume 10, Issue 7, Article R76
Research
A transcriptional network associated with natural variation in
Drosophila aggressive behavior
Alexis C Edwards
*†§
, Julien F Ayroles
*†
, Eric A Stone
†‡
,
Mary Anna Carbone
*†
, Richard F Lyman
*†
and Trudy FC Mackay
*†
Addresses:
*
Department of Genetics, North Carolina State University, Raleigh, North Carolina 27695, USA.
†
WM Keck Center for Behavioral
Biology, North Carolina State University, Raleigh, North Carolina 27695, USA.
‡
Department of Statistics, North Carolina State University,
Raleigh, North Carolina 27695, USA.
§
Current address: Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth
University, Department of Psychiatry, Richmond, VA 23298-0126, USA.

Correspondence: Trudy FC Mackay. Email:
© 2009 Edwards et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License ( which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A transcriptional network for aggression<p>A genome-wide screen of inbred Drosophila lines together with transcriptional network modeling reveals insights into the genetic bases of heritable aggression.</p>
Abstract
Background: Aggressive behavior is an important component of fitness in most animals.
Aggressive behavior is genetically complex, with natural variation attributable to multiple
segregating loci with allelic effects that are sensitive to the physical and social environment.
However, we know little about the genes and genetic networks affecting natural variation in
aggressive behavior. Populations of Drosophila melanogaster harbor quantitative genetic variation in
aggressive behavior, providing an excellent model system for dissecting the genetic basis of
naturally occurring variation in aggression.
Results: Correlating variation in transcript abundance with variation in complex trait phenotypes
is a rapid method for identifying candidate genes. We quantified aggressive behavior in 40 wild-
derived inbred lines of D. melanogaster and performed a genome-wide association screen for
quantitative trait transcripts and single feature polymorphisms affecting aggression. We identified
266 novel candidate genes associated with aggressive behavior, many of which have pleiotropic
effects on metabolism, development, and/or other behavioral traits. We performed behavioral
tests of mutations in 12 of these candidate genes, and show that nine indeed affected aggressive
behavior. We used the genetic correlations among the quantitative trait transcripts to derive a
transcriptional genetic network associated with natural variation in aggressive behavior. The
network consists of nine modules of correlated transcripts that are enriched for genes affecting
common functions, tissue-specific expression patterns, and/or DNA sequence motifs.
Conclusions: Correlations among genetically variable transcripts that are associated with genetic
variation in organismal behavior establish a foundation for understanding natural variation for
complex behaviors in terms of networks of interacting genes.
Published: 16 July 2009
Genome Biology 2009, 10:R76 (doi:10.1186/gb-2009-10-7-r76)
Received: 6 March 2009

Revised: 3 June 2009
Accepted: 16 July 2009
The electronic version of this article is the complete one and can be
found online at /> Genome Biology 2009, Volume 10, Issue 7, Article R76 Edwards et al. R76.2
Genome Biology 2009, 10:R76
Background
Animals display aggressive behaviors in defense of territory,
to secure and defend food and mates, and to establish domi-
nance hierarchies. These behaviors are, however, energeti-
cally costly and individually risky, suggesting that excessive
aggression may be deleterious. In humans, aggression often
manifests as violent behavior with attendant costs to society,
and is frequently a component of psychiatric disorders,
including schizophrenia, conduct disorder, alcoholism, bipo-
lar disorder, and Alzheimer's disease [1-4]. Analysis of muta-
tions and pharmacological treatments have established that
aggressive behavior is evolutionarily conserved and is modu-
lated by the neurotransmitters serotonin, dopamine, nore-
pinephrine, γ-aminobutyric acid, histamine and nitric oxide
as well as their receptors and transporters and key enzymes in
their biosynthetic pathways in mammals [5] and inverte-
brates [6]. However, these molecules are not the only players.
In mice, mutations in fierce, which encodes a nuclear recep-
tor [7], neural cell adhesion molecule [8], interleukin-6 [9]
and Cathepsin E [10] affect aggressive behavior. In Dro-
sophila, aggressive behavior is correlated with levels of β-
alanine [11,12], correct expression of sex-specific transcripts
of fruitless [13,14], biogenic amines [11,15], and expression of
neuropeptide F [15].
Levels of aggression vary continuously in natural popula-

tions, due to the segregation of alleles at multiple loci with
effects that depend on the social and physical environment:
aggressive behavior is thus a typical quantitative trait [16]. In
contrast to our understanding of the neurobiological and
genetic mechanisms responsible for the manifestation of
aggressive behavior, we know very little of the genes and
genetic networks affecting natural variation in aggression.
Hints that the genetic architecture of aggressive behavior may
be complex come from studies examining correlated
responses of the Drosophila transcriptome to artificial selec-
tion for aggressive behavior in a laboratory stock [17] and a
population recently derived from nature [18]. These studies
showed that the expression of 80 [17] to 1,539 transcripts [18]
involved in a wide variety of biological processes and molecu-
lar functions varied between the selected and control lines.
Subsequent analysis of the effects of mutations in genes
encoding some of these transcripts showed that Cyp6a20 [17]
and 15 other novel genes [18] (muscleblind, CG17154,
CG5966, CG30015, Darkener of apricot, CG14478, CG12292,
tramtrack, CG1623, CG13512, SP71, longitudinals lacking,
scribbler, Male-specific RNA 87F, kismet
) affect aggressive
behavior. However, the genotypes created by artificial selec-
tion are different from any naturally segregating genotype,
and it is possible that novel combinations of alleles perturb
the transcriptome beyond the range of variation that would
be found in a population of wild-type alleles. In addition,
selection induces linkage disequilibrium between selected
and linked loci, raising the possibility that some correlated
transcriptional responses to selection are due to linkage drag.

Here, we quantified male aggressive behavior for 40 inbred
lines derived from the same population, and performed a
genome-wide association scan for quantitative trait tran-
scripts (QTTs) [19] and single feature polymorphisms (SFPs)
[20] associated with aggressive behavior in wild-type geno-
types. This unbiased genomic approach reveals natural
genetic variation that is correlated with aggression at the level
of allelic differences and networks of genetically correlated
transcripts.
Results and discussion
Natural variation in aggressive behavior
We quantified aggressive behavior of 40 wild-derived inbred
lines, using a rapid and high-throughput behavioral assay
[19]. Variation in aggressive behavior was continuously dis-
tributed among these lines, as expected for a quantitative
trait. There was significant genetic variation in aggression
among lines (F
40,779
= 73.0168, P < 0.0001; Figure 1). Esti-
mates of among line (
σ
L
2
) and within line (
σ
E
2
) variance com-
ponents were
σ

L
2
= 0.783 and
σ
E
2
= 0.217, for a broad-sense
heritability (H
2
) of aggressive behavior of H
2
= 0.78. Surpris-
ingly, there was a 25-fold range of aggressive behavior in
these lines: from an average of 3.3 to 76.9 aggressive encoun-
ters for 8 flies in a 2-minute observation period.
The variation among the inbred lines far exceeds that of lines
selected for 21 generations for increased and decreased
aggressive behavior, which only differ less than threefold
(with a mean of 14.2 and 34.2 encounters in the high and low
selection lines using the same assay) [18]. Under a strictly
additive model, we expect variation among fully inbred lines
to be twice the additive genetic variation in the base popula-
tion from which they were derived [16]. Thus, under strict
additivity, the estimate of the narrow sense heritability (h
2
) in
this population would be h
2
= 0.64. This is much greater than
the estimate of realized h

2
from response to selection (h
2
≈
0.09) [18], indicating that alleles affecting natural variation
are recessive and/or interact epistatically.
Candidate genes for aggressive behavior
Previously, we quantified variation in gene expression among
these wild-derived inbred lines [21]. A total of 7,508 tran-
scripts were significantly variable among lines in males at a
false discovery rate (FDR) of < 0.01 and 3,316 probes con-
tained SFPs. We identified 133 QTTs (P < 0.01) associated
with variation in aggressive behavior (Additional data file 1).
In addition, 167 SFPs (P < 0.05) with a minor allele frequency
of at least 10% were associated with variation in aggression;
these represent 137 independent genes (Additional data file
2). Four of the QTTs were also implicated as candidates from
the SFP analysis (CG1146, CG2556, CG31038 and methuse-
lah-like 8). No gene ontology information is available for
three of these genes (CG1146, CG2556, and CG31038). meth-
uselah-like 8 encodes a predicted G protein coupled receptor
that may affect the determination of life span [22].
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Genome Biology 2009, 10:R76
In total, these analyses implicate 266 unique candidate genes
associated with natural variation in aggressive behavior.
These candidate genes are involved in a broad spectrum of
biological processes, including vision, olfaction, learning and
memory, and the development and function of the nervous
system (Additional data files 1 to 4). However, the candidate

genes are also involved in transcription, protein modification,
mitosis and other basic cellular processes (Additional data
files 1 to 4). More than half of the genes with annotations are
involved in metabolism, nearly 60% have protein binding
functions, and approximately 25% are implicated in develop-
ment (Additional data file 5) [23].
Two categories of candidate genes are worthy of mention. We
found a member of the Cytochrome P450 gene family associ-
ated with aggressive behavior, Cyp4p2. Members of this gene
family have also been associated with aggressive behavior in
previous studies [17,18]. Cytochrome P450s are generally
involved in oxidation, metabolism, protection from xenobiot-
ics, and possibly pheromone recognition [24]. The repeated
implication of this class of genes suggests that some or all of
these functions, or yet unknown functions of this class of pro-
teins, mediate aggressive behavior, although it remains
unclear precisely how. We also found three genes that have
been previously implicated in learning and/or memory to be
associated with natural variation in aggression in this screen
- nord, visgun, and klingon [25] - consistent with a previous
report that Drosophila aggressive behavior is associated with
learning and memory [26]. Perhaps variation in these genes
affects the fly's learning ability, which could subsequently
influence the behavioral response to aggressive encounters.
Assessment of these wild-derived lines in a learning and
memory assay could inform our understanding of the rele-
vance and variation of social memory in wild Drosophila.
A total of 26 of the 266 candidate genes identified in this
study overlapped with the candidate genes implicated from
the correlated response of the transcriptome to selection for

divergent level of aggressive behavior [18], from a different
sample of the same base population as the one from which the
inbred lines were derived (Additional data file 6). This is no
more overlap than expected by chance (χ
1
2
= 0.36, P > 0.05).
There are several possible - and not mutually exclusive - rea-
sons why the degree of overlap between the two experiments
is not more extensive. First, the observation that there is no
more overlap between the two experiments than expected by
chance could mean that there are many rare alleles affecting
aggressive behavior segregating in nature, such that two inde-
pendent samples captured different subsets of alleles. Sec-
ond, the flies from the selection lines were not mated, and had
been starved for 90 minutes prior to RNA extraction, in con-
trast to the mated, fully fed flies for which transcript profiles
were obtained in this experiment. Third, the control line was
the most extreme for many of the transcripts that were diver-
gent among the selection lines; this type of transcript-pheno-
type association will not be detected in a linear regression.
Fourth, selection causes linkage disequilibrium between the
selected locus and linked unselected loci; changes in tran-
script abundance among these linked loci between the selec-
tion lines are false positive associations. In contrast, the rapid
decay of linkage disequilibrium in regions of normal recombi-
nation in unselected Drosophila [27,28] minimizes false pos-
itive associations of transcript abundance of linked loci in the
unselected inbred lines. Fifth, a greater fraction of the genetic
Variation in aggressive behavior among 40 wild-derived inbred linesFigure 1

Variation in aggressive behavior among 40 wild-derived inbred lines. The line number is indicated on the x-axis, and the mean aggression score (MAS) on
the y-axis. Error bars are standard error.
0
10
20
30
40
50
60
70
80
90
303
335
517
712
315
730
360
307
380
705
427
379
304
437
375
555
365
799

486
765
357
158
714
786
313
399
301
639
306
391
820
732
362
208
514
774
358
324
707
852
Line
MAS
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Genome Biology 2009, 10:R76
variation among the inbred lines than the selection lines is
due to dominance and epistasis. The transcriptional signa-
ture of a homozygous recessive allele in the inbred lines is
likely to be different from the same allele as a heterozygote in

the selection lines. Thus, the overlap of genes between the two
studies may be enriched for loci with additive effects that
causally affect natural variation in aggressive behavior.
Functional tests
To evaluate whether the candidate genes suggested from
these analyses potentially affect aggressive behavior, we
assessed aggression levels of P-element insertional mutations
in 12 of the candidate genes, and their co-isogenic control
lines. Nine of the mutant alleles were associated with signifi-
cantly different aggression levels from the control (Figure 2).
This high 'success' rate shows that expression profiling of
wild-derived genetically divergent lines is an efficient method
for identifying candidate genes affecting complex traits, as
has been observed previously [17,18,29-31].
Flies with mutations in CG11448, CG13760, CG2556,
CG31038, CG32425, late bloomer and skuld are all more
aggressive than their controls, while flies with mutations in
GTPase-activating protein 1 (Gap1) and schizo are less
aggressive than the control strain. No gene ontology informa-
tion is available for the predicted genes tested; however,
CG11448 is homologous to the amyloid beta A4 precursor
protein, which is implicated in Alzheimer's disease. late
bloomer has a role in nervous system development and syn-
apse biogenesis. It is homologous to TSPAN7, a tetraspanin
protein implicated in mental retardation [32]. skuld is
involved in numerous transcription-related processes, and
also has roles in metabolism and development. Gap1 has roles
in the cell cycle, and is also involved in signal transduction
and numerous developmental processes, such as axis specifi-
cation and sensory organ development. Finally, schizo is

involved in several signal transduction pathways, the devel-
opment of the central nervous system, and muscle develop-
ment. It is homologous to the human protein ADP-
ribosylation factor guanine nucleotide exchange factor 2, dys-
functions of which are associated with microcephaly [33].
Transcriptional network associated with aggression
The transcriptome is highly genetically inter-correlated [21].
This correlation structure can be used to infer modules of
genetically correlated transcripts associated with aggressive
behavior, after removing the correlations among the tran-
scripts attributable to their association with aggression itself.
The number and contents of modules are determined such
that the average correlation of probe sets within a module is
maximized, while the average correlation among probe sets in
different modules is minimized. The 133 QTTs grouped into 9
modules, ranging in size from 2 to 54 probe sets (Figure 3a;
Additional data file 7). The correlated transcript modules
associated with aggressive behavior can also be represented
as an interaction network, with edges between transcripts in
the network determined by genetic correlations in transcript
abundance exceeding a threshold value (Figure 3b represents
|r| ≥ 0.7). Note that these are, at present, undirected net-
works. We do not know which transcripts are causally associ-
ated with variation in aggression, due to functional
polymorphisms in cis-regulatory regions, and which tran-
scripts are trans-regulated and change expression as a conse-
quence of cis-regulatory variation at another locus [34].
We evaluated the biological plausibility of the modules by
querying whether genes in the modules are enriched for
shared gene ontology categories, tissue-specific expression

patterns, or DNA sequence motifs (the latter using the Multi-
ple EM for Motif Elicitation (MEME) tool). Approximately
one-third of the transcripts in module 6 affect ion binding,
relative to approximately 2% of the probe sets in the genomic
background; this is a significant enrichment (P < 0.01).
Nearly 50% of the annotated genes in module 6 are involved
in establishment of localization, compared to approximately
13% of the background (P < 0.001); 25 to 30% of the genes in
modules 6 (P < 0.05) and 7 (P < 0.01) are involved in cell com-
munication, whereas only 13% of the background falls into
that category (Figure 4). Module 7 is enriched for several cat-
egories related to development (Figure 4). Transcripts in
modules 6 and 7 are enriched in the brain, head, and thoraci-
coabdominal ganglion (Figure 5), indicating that these genes
function primarily in central nervous system functions. How-
ever, the fact that they fall into distinct modules suggests that
their specific functions differ, or that they are differentially
regulated in a temporally or spatially specific manner.
Aggression levels in P-element mutantsFigure 2
Aggression levels in P-element mutants. Mean deviation from control
levels of aggression is depicted (± standard error). Red bars indicate
significantly higher aggression (P < 0.05); blue bars indicate significantly
lower aggression; and green bars indicate lines that did not differ
significantly from control.
-10
-5
0
5
10
15

20
Deviation from control
CG11448
CG13760
CG13928
CG2556
CG31038
CG32425
dpr16
Esterase-10
Gap1
late bloomer
schizo
skuld
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Genome Biology 2009, 10:R76
Modules of correlated transcripts associated with variation in aggressive behaviorFigure 3
Modules of correlated transcripts associated with variation in aggressive behavior. (a) Heat map of correlated probe sets after module formation. The
strength of the module decreases down the diagonal. (b) Network view of the most highly correlated (r ≥ 0.7) probe sets where the edges represent
correlated transcripts and the color-coding of nodes represents the different modules depicted in (a).
20 40 60 80 100 120
20
40
60
80
100
120
(a)
(b)
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Genome Biology 2009, 10:R76
Additional support for the hypothesis that genes in a module
are co-regulated is generated by shared MEMEs among mem-
bers of a module [35] (Figure 6). The P-value for each gene
containing the consensus sequence represents the probability
of a random sequence having the same match score or higher.
Of 35 genes in module 6, 29 share a motif with a 20-bp con-
sensus sequence, and the significance values for genes con-
taining this motif range from P = 2.68 × 10
-4
to P = 1.82 × 10
-
10
(Figure 6a). Of 54 genes in module 7, 18 share a 14-bp motif,
with P-values ranging from P = 9.32 × 10
-6
to P = 4.73 × 10
-9
(Figure 6b).
Although many of the QTTs lack annotation, we can infer
potential functions based on the characterized genes that fall
into the same correlated module. Three of the four QTTs in
module 1 belong to a large transcriptional module enriched
for male biased transcripts [21], and these genes are highly
expressed in the testis [36]; perhaps this module is related
specifically to male reproductive functions. Of the five QTTs
in module 4, three are involved in visual perception. Their
correlated expression implies that the others, CG13928 and
CG6403, might share a similar function. The fact that all of
these transcripts are highly expressed in the head supports

this possibility (Figure 5). Three of the four annotated genes
in module 8 are involved in metabolic functions, suggesting a
similar role for the uncharacterized genes in that module.
Differences in Gene Ontology representation between modulesFigure 4
Differences in Gene Ontology representation between modules. All categories depicted are statistically over- or under-represented in module 6 and/or 7
relative to the appropriate genomic background. Asterisks indicate significance levels in module 6, while plus symbols (+) indicate significance in module 7.
For example, genes involved in the cell cycle are significantly (P < 0.05) under-represented in module 7. */+, P < 0.05; **/++, P < 0.01; ***/+++, P < 0.001.
0 5 10 15 20 25 30 35 40 45 50
Biosynthetic process
Catabolic process
Cell adhesion
Cell communication
Cell cycle
Cell division
Developmental maturation
Establishment of localization
Localization of cell
Membrane docking
Neurological process
Response to chemical stimulus
Module 6 Module 7 Background
++
*
+++
**
++
*
+
***
***

***
+++
***
*
+
Module-specific enrichment scores in adult tissues, based on data from FlyAtlas [36]Figure 5
Module-specific enrichment scores in adult tissues, based on data from
FlyAtlas [36]. Acc. Gland, accessory gland.
1
3
5
7
9
0
2
4
6
8
10
12
14
16
18
Mean enrichment
Module
Brain
Head
Crop
Midgut
Hindgut

Tubule
Ovary
Testes
Acc. gland
TAG
Carcass
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Conserved motifs in modules 6 and 7Figure 6
Conserved motifs in modules 6 and 7. (a,b) The motifs most frequently found among genes in modules 6 (a) and 7 (b) are shown. The frequency of each
nucleotide at each position is depicted on the y-axis, with the nucleotide position within the consensus sequence depicted on the x-axis. The motif in (a)
was contained within 29 of 35 genes in module 6; the motif in (b) was contained in 18 of 54 genes in module 7. Significance level of adherence to the
consensus sequence was at least P = 2.68 × 10
-4
for (a) and P = 9.32 × 10
-6
for (b).
0%`
20%
40%
60%
80%
100%
1234567891011121314151617181920
T
G
C
A
Position within motif
0%

20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14
T
G
C
A
Position within motif
(a)
(b)
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Additional tests can help us tease apart the relationships
among genes within a module. For example, manipulation of
a single gene and assessment of the effects on other genes
within the same module can elucidate causality and direction
of effects.
Pleiotropy
The wild-derived inbred lines have been assessed for varia-
tion in other complex traits: longevity, starvation stress
resistance, chill coma recovery time, locomotor reactivity (a
startle response), copulation latency, competitive fitness and
sleep traits [21,37]. At the level of organismal phenotype, only
locomotor reactivity was significantly genetically correlated
with aggressive behavior (r
G
= 0.49, P < 0.001). However,

organismal genetic correlations can only be significant if alle-
les affecting both traits have largely similar positive or nega-
tive effects on the traits [16]. There can be substantial
pleiotropy in the absence of genetic correlation if alleles at
many loci affect both traits, but the sign of the effects is not
correlated. This motivated us to ask whether particular mod-
ules of transcripts associated with aggressive behavior were
associated with modules of transcripts associated with the
other traits (Additional data file 8). Many of the probe sets
implicated in multiple traits correspond to predicted genes
about which little is known. However, transcript abundance
of synaptogyrin, which is involved in synaptic vesicle exocy-
tosis [38], is associated with variation in starvation resistance
and fitness [21]. Rab9 is associated with chill coma recovery
[21] and sleep [37]. GRHRII, which encodes a predicted G-
protein coupled receptor [39] and gonadotropin-releasing
hormone receptor [40], is associated with starvation resist-
ance [21] and sleep [37].
In addition to examining genetic correlations between QTTs
affecting aggressive behavior, we can ask which of the genes
affecting aggressive behavior are most highly correlated (r ≥
0.70) to the transcriptome (Figure 3b). Three QTTs stand out
as being highly connected. miple transcript abundance is
highly correlated with 22 other transcripts. It is highly pleio-
tropic, and is thought to affect locomotor behavior, muscle
development, ATP binding, synapse biogenesis, and response
to stimulus [22]. VAChT expression is correlated with 21
other transcripts. It is described as an acetylcholine trans-
porter, and is also involved in the response to a chemical
stimulus [22]. Another 'hub' gene is unc-104, which falls into

many of the Gene Ontology categories described for miple; it
is also involved in nucleotide binding. Mutations in human
homologues have been implicated in spastic paraplegia and
Charcot-Marie-Tooth disease [22]. Additional highly con-
nected genes are the computationally predicted genes
CG2790, CG13928, CG14853, and CG6156, about which little
annotation information is available, although CG2790 and
CG13928 are reportedly involved in zinc ion and protein
binding.
Expression of all of these integral genes is highly enriched in
the brain, head, and thoracicoabdominal ganglion. Further-
more, the male accessory glands exhibit enrichment of unc-
104, and CG6156 is up-regulated in the crop, tubule, larval
tubule, and larval fat body [36]. Their high degree of connec-
tivity implies that these genes might be central to networks
involved in aggressive behavior. The range of biological proc-
esses and molecular functions in which they are involved
makes it difficult to isolate which are relevant to aggression,
but their high expression levels in the head and nervous sys-
tem unsurprisingly implicate those tissues in the modulation
of aggression. We can also use these data to develop hypothe-
ses about the highly connected yet uncharacterized genes
CG2790, CG13928, CG14853
, and CG6156.
Insights about the genetic architecture of aggressive
behavior
Aggression is clearly a highly complex trait - we have identi-
fied 266 candidate genes associated with natural variation in
aggressive behavior, none of which have been previously
implicated to affect aggression. Follow-up functional valida-

tion shows that 75% of P-element insertional mutations
tested in these candidate genes indeed affect aggression. The
candidate genes embrace a wide range of biological functions
with plausible connections to aggressive behavior (sensory
perception and chemosensation, function and development
of the nervous system), as well as other general functions with
less obvious relationships to aggression per se (metabolism,
protein modification, mitosis). Analysis of natural variants
affecting complex traits that have survived the sieve of natural
selection thus gives insights about the genetic basis of com-
plex behaviors that are not possible from analysis of muta-
tions of large effect. That none of the genes previously
implicated to affect aggression was detected in this screen is
somewhat surprising. There are several possible explana-
tions. The known candidate genes may not be genetically var-
iable at the level of transcription; we could not detect
genetically variable transcripts at these loci because they are
expressed at low levels or at a different developmental stage;
our SFP map detects only a small fraction of polymorphic var-
iants; and the candidate genes may not tolerate functional
variation due to strong purifying selection. For example, var-
iation in fruitless was not associated with variation in aggres-
sive behavior in this study or previous studies [17,18]. Only
one of the seven probe sets on the array that target fruitless
was genetically variable, and variation in fruitless expression
for this probe set was not associated with variation in aggres-
sive behavior.
The QTTs associated with natural variation in aggressive
behavior group into genetically correlated modules with
shared functional annotations, sequence motifs, and tissue-

specific expression. These modules are, in turn, correlated
with other traits, providing insights about the molecular basis
of pleiotropy between aggression and other behavioral and
fitness-related traits. These results provide the foundation for
Genome Biology 2009, Volume 10, Issue 7, Article R76 Edwards et al. R76.9
Genome Biology 2009, 10:R76
a systems genetics analysis of natural variation in aggressive
behavior. The future availability of whole genome DNA
sequence variation for these lines will enable us to discrimi-
nate cis- from trans-acting polymorphisms, and infer the
direction of the flow of information through the network. The
entire suite of 266 candidate genes provides a focal point for
linkage analysis of segregating populations derived from the
inbred lines. Further, the inbred lines can be characterized for
other quantitative traits, including components of metabo-
lism, which will enable us to interpret the balance of selective
forces maintaining variation for aggressive behavior in natu-
ral populations on a genome wide scale. Extension of these
analyses to a larger sample of inbred lines will increase the
power of network analyses, and provide a more representa-
tive sample of allelic diversity associated with aggressive
behavior. Finally, it is not inconceivable that our understand-
ing the genetic underpinnings of variation in aggressive
behavior in Drosophila could be used to develop novel phar-
macological therapies for treatment of pathological aggres-
sion in humans and domestic animals.
Conclusions
Aggressive behavior is an important component of fitness in
most animals, and is genetically complex, with natural varia-
tion attributable to multiple segregating loci with allelic

effects that are sensitive to the physical and social environ-
ment. However, we know little about the genes and genetic
networks affecting natural variation in aggressive behavior.
We combined quantitative genetic analysis of variation in
aggressive behavior with whole genome transcript profiling
in a population of D. melanogaster inbred lines to identify
266 novel candidate genes associated with aggressive behav-
ior, many of which have pleiotropic effects on metabolism,
development, and/or other behavioral traits. Behavioral tests
of mutations in 12 of these candidate genes showed that 9
indeed affected aggressive behavior. The genetically corre-
lated transcripts formed a transcriptional genetic network of
nine modules of correlated transcripts that are enriched for
genes affecting common functions, tissue-specific expression
patterns, and/or DNA sequence motifs. These results estab-
lish a foundation for understanding natural variation for
complex behaviors in terms of networks of interacting genes.
Materials and methods
Drosophila strains
The 40 inbred lines were derived by 20 generations of full-sib
mating from isofemale lines that were collected from the
Raleigh, NC farmer's market in 2003 [21]. Flies were reared
under standard culture conditions on cornmeal-molasses-
agar medium at 25°C, 60 to 75% relative humidity, on a 12-h
light-dark cycle. P-element insertional mutations and their
co-isogenic control lines were obtained from Bloomington
Drosophila Stock Center, Bloomington, Indiana, USA.
Behavioral assay
Behavioral assays were performed as described previously
[18] on socially experienced, 3- to 7-day-old males. Flies were

not exposed to anesthesia for at least 24 h prior to the assay.
A total of 20 replicate assays were performed for each line,
with one replicate per line per day for a total of 20 days. Each
replicate consisted of a group of eight 3- to 7-day-old flies of
the same genotype. The flies were placed in a vial without
food for 90 minutes, after which they were transferred (with-
out anesthesia) to a test arena containing a droplet of food
and allowed to acclimate for 2 minutes. After the acclimation
period, the flies were observed for 2 minutes; the aggression
score of each replicate was the total number of aggressive
interactions observed among all eight flies in the 2-minute
observation period. Behavioral assays were conducted in a
behavioral chamber (25°C, 70% humidity) between 8 a.m.
and 11 a.m.
Whole genome expression analysis
The gene expression analysis has been described previously
[21]. Briefly, RNA was extracted from two independent pools
of 25 3- to 5-day-old mated whole flies/sex/line that were fro-
zen at the same time of day, labeled, and hybridized to
Affymetrix Drosophila 2.0 arrays, using a strictly randomized
experimental design. The raw array data were normalized
using a median standardization. The measure of expression
was the median log2 signal intensity of the probes in the per-
fect match probe sets, after removing probes containing SFPs
between the wild-derived lines and the reference strain
sequence used to design the array. Negative control probes
were used to estimate the level of background intensity; probe
sets with expression levels below this threshold were consid-
ered to be not expressed.
Quantitative genetic analyses

The analysis of variance (ANOVA) model Y =
μ
+ L +
ε
was
used to partition variation in male aggressive behavior and
transcript abundance between lines (L, random) and the var-
iation within lines (
ε
). A FDR of < 0.01 [41] was used to assess
significance of the L term in the analyses of natural variation
in gene expression, to account for multiple testing. Broad
sense heritabilities (H
2
) were estimated as:
- where
σ
L
2
and
σ
E
2
are the among line and within line vari-
ance components, respectively. Estimate of cross-trait genetic
correlations were:
- where cov
ij
is the covariance of line means between trait i
and trait j, and

σ
i
and
σ
j
are the square roots of the among line
variance components for the two traits. Differences in aggres-
sive behavior between P-element insert lines and their co-iso-
genic controls were assessed by t-tests, with significance
H
LLE
2222
=+
σσσ
/( )
rcov
ijGij
= /
σσ
Genome Biology 2009, Volume 10, Issue 7, Article R76 Edwards et al. R76.10
Genome Biology 2009, 10:R76
levels based on Bonferroni-corrected P-values. Simple linear
regressions were used to identify QTTs significantly associ-
ated (P < 0.01) with variation in aggressive behavior across
the 40 lines. Similarly, ANOVA models (Y =
μ
+ M +
ε
, where
M denotes SFP presence or absence) were used to identify

SFPs significantly associated (P < 0.05) with variation in
aggressive behavior.
Transcriptional networks
The genetic correlations between all transcripts significantly
associated with aggressive behavior were computed after
removing the correlation between these transcripts and the
phenotype. This was achieved by fitting the model Y =
μ
+ E +
ε
(Y is the phenotype and E is the covariate median log2
expression level) and extracting the residuals to compute the
genetic correlations for module construction [21]. Modules of
transcripts associated with aggressive behavior with coordi-
nated patterns of expression across the 40 lines were then
quantified as described previously [21] by transforming the
pairwise genetic correlations among transcripts into Eucli-
dean-like distances, which were used to construct an affinity
matrix. The transcripts were partitioned into modules using a
graph-theoretical approach that envisions the transcripts as
nodes in an undirected graph whose edges are weighted by
the entries of the affinity matrix. Transcriptional modules
common to aggressive behavior and other phenotypes meas-
ured on the 40 wild-derived inbred lines [21,37] were identi-
fied by comparing the transcripts in each aggression module
to the transcripts in each module from the other phenotypes,
and determining whether the overlap between the modules
exceed what is expected by chance using a Fisher's exact test
[21].
Bioinformatics

Statistical analyses were performed using JMP 7.0 software
(SAS, Cary, NC, USA). Functional annotations of genes are
based on FlyBase [38] annotations; additional information
was obtained using FlyMine v12.0 [22] and Babelomics v2
and v3 [23]. Categories that were represented by fewer than
5% of the gene list queried were excluded. Statistically signif-
icant over- or under-representation was determined by the
online software used when available; otherwise, a chi-square
test was performed, using the appropriate genomic back-
ground to determine the expected values.
Abbreviations
ANOVA: analysis of variance; FDR: false discovery rate;
MEME: Multiple EM for Motif Elicitation; QTT: quantitative
trait transcript; SFP: single feature polymorphism.
Authors' contributions
TFCM and ACE designed research; ACE performed research;
EAS contributed analytic tools; ACE and JFA analyzed data;
TFCM and ACEF wrote the paper.
Additional data files
The following additional data are available with the online
version of this paper: transcripts significantly associated with
variation in aggressive behavior among 40 wild-derived
inbred lines (regression P < 0.01; Additional data file 1); asso-
ciations of SFPs with aggressive behavior (Additional data file
2); Gene Ontologies represented by quantitative trait tran-
scripts (Additional data file 3); Gene Ontologies represented
by probe sets containing SFPs (Additional data file 4); Gene
Ontology categories represented by genes associated with
male aggressive behavior through either the identification of
SFPs or transcript abundance (Additional data file 5); candi-

date genes previously associated with aggressive behavior
[18] (Additional data file 6); analysis of modules of correlated
transcripts associated with aggressive behavior (Additional
data file 7); pleiotropic genes affecting aggression (Additional
data file 8).
Additional data file 1Transcripts significantly associated with variation in aggressive behavior among 40 wild-derived inbred linesMean expression level, among (
σ
L
2
) and within (
σ
E
2
) line variance components, broad sense heritabilities (H
2
) and the false discovery rate (FDR) for the line term are for males only.Click here for fileAdditional data file 2Associations of SFPs with aggressive behaviorThe P-value is from the ANOVA of the difference in trait means between the two SFP classes. a is one half the difference in trait mean between the SFP alleles. MAF = minor allele frequency; MAS = mean aggression score.Click here for fileAdditional data file 3Gene Ontologies represented by quantitative trait transcriptsLevel 3 (a) biological process and (b) molecular function Gene Ontology categories of genes for which variation in gene expression is correlated with variation in aggressive behavior. The percentage of genes falling into a given category is depicted on the x-axis. The relevant genomic background is the 7,508 probe sets that were dif-ferentially expressed in males at a FDR of < 0.01. No categories were significantly over-represented among level 3 categories; how-ever, the level 4 'transport' biological process category was over-represented (adjusted P = 0.0364, data not shown).Click here for fileAdditional data file 4Gene Ontologies represented by probe sets containing SFPs(a) Level 3 biological process and (b) level 4 molecular function categories. Only categories applying to at least 5% of a list are depicted. Categories significantly over-represented in the SFP list relative to the genomic background are denoted as follows: *adjusted P < 0.05; **P < 0.01; ***P < 0.001.Click here for fileAdditional data file 5Gene Ontology categories represented by genes associated with male aggressive behavior through either the identification of SFPs or transcript abundanceCategories in (a) are level 3 biological processes; those in (b) are level 3 molecular functions. The percent of genes falling into a given category is depicted on the y-axis.Click here for fileAdditional data file 6Candidate genes previously associated with aggressive behavior [18]Average |r| is the mean absolute value of the correlation of the tran-script to all other variable transcripts. H
2
= broad sense heritability. QTT = quantitative trait transcript; SFP = single feature polymor-phism.Click here for fileAdditional data file 7Analysis of modules of correlated transcripts associated with aggressive behaviorDegree = the average correlation of a transcript with all other tran-scripts in its module. Average degree = the average correlation of all transcripts in the module.Click here for fileAdditional data file 8Pleiotropic genes affecting aggressionExpression of these genes has been correlated with aggressive behavior and the other traits listed. (a) Reference [21]; (b) refer-ence [37].Click here for file
Acknowledgements
This work was funded by NIH grants F31 MH-074161 to ACE and R01 GM-
076083 and R01 GM45146 to TFCM. This is a publication of the WM Keck
Center for Behavioral Biology.
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